scholarly journals The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

2013 ◽  
Vol 19 (6) ◽  
pp. 659-667 ◽  
Author(s):  
A Di Martino ◽  
C-G Yan ◽  
Q Li ◽  
E Denio ◽  
F X Castellanos ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Data Exchange ◽  
Large Scale ◽  
Imaging Data ◽  
Scale Evaluation ◽  
Brain Architecture ◽  
Brain Imaging Data

scholarly journals Cerebellar Volume in Autism: Literature Meta-analysis and Analysis of the Autism Brain Imaging Data Exchange Cohort

2018 ◽  
Vol 83 (7) ◽  
pp. 579-588 ◽  
Author(s):  
Nicolas Traut ◽  
Anita Beggiato ◽  
Thomas Bourgeron ◽  
Richard Delorme ◽  
Laure Rondi-Reig ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Data Exchange ◽  
Meta Analysis ◽  
Imaging Data ◽  
Cerebellar Volume ◽  
Brain Imaging Data

scholarly journals COINSTAC: Decentralizing the future of brain imaging analysis

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1512 ◽  
Author(s):  
Jing Ming ◽  
Eric Verner ◽  
Anand Sarwate ◽  
Ross Kelly ◽  
Cory Reed ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Data Sharing ◽  
Large Scale ◽  
Data Transfer ◽  
Imaging Data ◽  
Sensitive Data ◽  
Large Scale Data ◽  
Decentralized Algorithms ◽  
Neuroimaging Data ◽  
Brain Imaging Data

In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications.

scholarly journals Enhancing studies of the connectome in autism using the autism brain imaging data exchange II

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Adriana Di Martino ◽  
David O’Connor ◽  
Bosi Chen ◽  
Kaat Alaerts ◽  
Jeffrey S. Anderson ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Data Exchange ◽  
Imaging Data ◽  
Brain Imaging Data

scholarly journals Cross-species machine learning improves diagnostic classification of human psychiatric disorders

2019 ◽  
Author(s):  
Yafeng Zhan ◽  
Jianze Wei ◽  
Jian Liang ◽  
Xiu Xu ◽  
Ran He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Psychiatric Disorders ◽  
Brain Imaging ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Diagnostic Classification ◽  
Imaging Data ◽  
Primate Model ◽  
Compulsive Disorder ◽  
Brain Imaging Data

AbstractPsychiatric disorders often exhibit shared (co-morbid) symptoms, raising controversies over accurate diagnosis and the overlap of their neural underpinnings. Because the complexity of data generated by clinical studies poses a formidable challenge, we have pursued a reductionist framework using brain imaging data of a transgenic primate model of autism spectrum disorder (ASD). Here we report an interpretable cross-species machine learning approach which extracts transgene-related core regions in the monkey brain to construct the classifier for diagnostic classification in humans. The cross-species classifier based on core regions, mainly distributed in frontal and temporal cortex, identified from the transgenic primate model, achieved an accuracy of 82.14% in one clinical ASD cohort obtained from Autism Brain Imaging Data Exchange (ABIDE-I), significantly higher than the human-based classifier (61.31%, p < 0.001), which was validated in another independent ASD cohort obtained from ABIDE-II. Such monkey-based classifier generalized to achieve a better classification in obsessive-compulsive disorder (OCD) cohorts, and enabled parsing of differential connections to right ventrolateral prefrontal cortex being attributable to distinct traits in patients with ASD and OCD. These findings underscore the importance of investigating biologically homogeneous samples, particularly in the absence of real-world data adequate for deconstructing heterogeneity inherited in the clinical cohorts.One Sentence SummaryFeatures learned from transgenic monkeys enable improved diagnosis of autism-related disorders and dissection of their underlying circuits.

scholarly journals Hype versus hope: Deep learning encodes more predictive and robust brain imaging representations than standard machine learning

2020 ◽  
Author(s):  
Anees Abrol ◽  
Zening Fu ◽  
Mustafa Salman ◽  
Rogers Silva ◽  
Yuhui Du ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Imaging ◽  
Large Scale ◽  
Representation Learning ◽  
Training Sample ◽  
Superior Performance ◽  
Computational Time ◽  
Imaging Data ◽  
Brain Imaging Data

AbstractPrevious successes of deep learning (DL) approaches on several complex tasks have hugely inflated expectations of their power to learn subtle properties of complex brain imaging data, and scale to large datasets. Perhaps as a reaction to this inflation, recent critical commentaries unfavorably compare DL with standard machine learning (SML) approaches for the analysis of brain imaging data. Yet, their conclusions are based on pre-engineered features which deprives DL of its main advantage: representation learning. Here we evaluate this and show the importance of representation learning for DL performance on brain imaging data. We report our findings from a large-scale systematic comparison of SML approaches versus DL profiled in a ten-way age and gender-based classification task on 12,314 structural MRI images. Results show that DL methods, if implemented and trained following the prevalent DL practices, have the potential to substantially improve compared to SML approaches. We also show that DL approaches scale particularly well presenting a lower asymptotic complexity in relative computational time, despite being more complex. Our analysis reveals that the performance improvement saturates as the training sample size grows, but shows significantly higher performance throughout. We also show evidence that the superior performance of DL is primarily due to the excellent representation learning capabilities and that SML methods can perform equally well when operating on representations produced by the trained DL models. Finally, we demonstrate that DL embeddings span a comprehensible projection spectrum and that DL consistently localizes discriminative brain biomarkers, providing an example of the robustness of prediction relevance estimates. Our findings highlight the presence of non-linearities in brain imaging data that DL frameworks can exploit to generate superior predictive representations for characterizing the human brain, even with currently available data sizes.

Dimensionality Reduction Methods for Brain Imaging Data Analysis

2021 ◽  
Vol 54 (4) ◽  
pp. 1-36
Author(s):  
Yunbo Tang ◽  
Dan Chen ◽  
Xiaoli Li
Keyword(s):  
Big Data ◽  
Dimensionality Reduction ◽  
Brain Imaging ◽  
Large Scale ◽  
Past Century ◽  
Complex Object ◽  
Imaging Data ◽  
Reduction Methods ◽  
Brain Imaging Data ◽  
Data Elements

The past century has witnessed the grand success of brain imaging technologies, such as electroencephalography and magnetic resonance imaging, in probing cognitive states and pathological brain dynamics for neuroscience research and neurology practices. Human brain is “the most complex object in the universe,” and brain imaging data ( BID ) are routinely of multiple/many attributes and highly non-stationary. These are determined by the nature of BID as the recordings of the evolving processes of the brain(s) under examination in various views. Driven by the increasingly high demands for precision, efficiency, and reliability in neuro-science and engineering tasks, dimensionality reduction has become a priority issue in BID analysis to handle the notoriously high dimensionality and large scale of big BID sets as well as the enormously complicated interdependencies among data elements. This has become particularly urgent and challenging in this big data era. Dimensionality reduction theories and methods manifest unrivaled potential in revealing key insights to BID via offering the low-dimensional/tiny representations/features, which may preserve critical characterizations of massive neuronal activities and brain functional and/or malfunctional states of interest. This study surveys the most salient work along this direction conforming to a 3-dimensional taxonomy with respect to (1) the scale of BID , of which the design with this consideration is important for the potential applications; (2) the order of BID , in which a higher order denotes more BID attributes manipulatable by the method; and (3) linearity , in which the method’s degree of linearity largely determines the “fidelity” in BID exploration. This study defines criteria for qualitative evaluations of these works in terms of effectiveness, interpretability, efficiency, and scalability. The classifications and evaluations based on the taxonomy provide comprehensive guides to (1) how existing research and development efforts are distributed and (2) their performance, features, and potential in influential applications especially when involving big data. In the end, this study crystallizes the open technical issues and proposes research challenges that must be solved to enable further researches in this area of great potential.

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